Examples of data clustering methods include k-means clustering. According to this method, the number K of clusters has to be designated, and thus the value K significantly affects the level of precision of the clustering. Setting parameters such as the number K is dependent on individual skills based on experiences and intuitions, and is an operation that requires an inordinate amount of effort.
The obtaining an optimal parameter value in order to obtain a clustering result with a high level of precision from data targeted for clustering can be regarded as obtaining a solution to a combinatorial optimization problem. Various combinatorial optimization problems and solutions thereto have been proposed. For example, a method for calculating self-organizing maps as in Non-Patent Document 1 and a method for calculating tug-of-war models as in Non-Patent Document 2 are also examples of methods for solving combinatorial optimization problems.